{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "![](https://raw.githubusercontent.com/Qinbf/tf-model-zoo/master/README_IMG/01.jpg)\n",
    "AI MOOC： **www.ai-xlab.com**  \n",
    "如果你也是AI爱好者，可以添加我的微信一起交流：**sdxxqbf**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "    from sklearn.preprocessing import PolynomialFeatures\n",
    "    from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAD8CAYAAACyyUlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqZphIkmqZphIkqoZJpKkaoaJJKna/wNCO/SrrN5SrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2538e73b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 载入数据\n",
    "data = np.genfromtxt(\"job.csv\", delimiter=\",\")\n",
    "x_data = data[1:,1]\n",
    "y_data = data[1:,2]\n",
    "plt.scatter(x_data,y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_data = x_data[:,np.newaxis]\n",
    "y_data = y_data[:,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 2.],\n",
       "       [ 3.],\n",
       "       [ 4.],\n",
       "       [ 5.],\n",
       "       [ 6.],\n",
       "       [ 7.],\n",
       "       [ 8.],\n",
       "       [ 9.],\n",
       "       [10.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建并拟合模型\n",
    "model = LinearRegression()\n",
    "model.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8KD4nvr7M3T22j4urvYYC1cD/AM8D1XHlVq84xqK4TVtjiEgxeO+9cG3IX/0VnH02/Pu/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\nFCYiIpKYwkRERBJTmIiISGL/B/eMZqtjafaVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2538e73b128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.plot(x_data, y_data, 'b.')\n",
    "plt.plot(x_data, model.predict(x_data), 'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义多项式回归,degree的值可以调节多项式的特征\n",
    "poly_reg  = PolynomialFeatures(degree=5) \n",
    "# 特征处理\n",
    "x_poly = poly_reg.fit_transform(x_data)\n",
    "# 定义回归模型\n",
    "lin_reg = LinearRegression()\n",
    "# 训练模型\n",
    "lin_reg.fit(x_poly, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00,\n",
       "        1.0000e+00],\n",
       "       [1.0000e+00, 2.0000e+00, 4.0000e+00, 8.0000e+00, 1.6000e+01,\n",
       "        3.2000e+01],\n",
       "       [1.0000e+00, 3.0000e+00, 9.0000e+00, 2.7000e+01, 8.1000e+01,\n",
       "        2.4300e+02],\n",
       "       [1.0000e+00, 4.0000e+00, 1.6000e+01, 6.4000e+01, 2.5600e+02,\n",
       "        1.0240e+03],\n",
       "       [1.0000e+00, 5.0000e+00, 2.5000e+01, 1.2500e+02, 6.2500e+02,\n",
       "        3.1250e+03],\n",
       "       [1.0000e+00, 6.0000e+00, 3.6000e+01, 2.1600e+02, 1.2960e+03,\n",
       "        7.7760e+03],\n",
       "       [1.0000e+00, 7.0000e+00, 4.9000e+01, 3.4300e+02, 2.4010e+03,\n",
       "        1.6807e+04],\n",
       "       [1.0000e+00, 8.0000e+00, 6.4000e+01, 5.1200e+02, 4.0960e+03,\n",
       "        3.2768e+04],\n",
       "       [1.0000e+00, 9.0000e+00, 8.1000e+01, 7.2900e+02, 6.5610e+03,\n",
       "        5.9049e+04],\n",
       "       [1.0000e+00, 1.0000e+01, 1.0000e+02, 1.0000e+03, 1.0000e+04,\n",
       "        1.0000e+05]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eeQVGjYJhw+Dtt2H0aJgyBT7zmaJbZg1wEDKzyjBvHnzlK7DnnvDYY/CjH8Fr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\noFSjKfDO6goAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253919662b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.plot(x_data, y_data, 'b.')\n",
    "plt.plot(x_data, lin_reg.predict(poly_reg.fit_transform(x_data)), c='r')\n",
    "plt.title('Truth or Bluff (Polynomial Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FR9SuOAmZmdVk1CjYd980GrYTULNwEjIzq87tt6cu2L17w7hxTkDNxH0LzcyA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\nnITMzKwwTkJmZlaY/w+0j2BjhWCsgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x253bfddecf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plt.plot(x_data, y_data, 'b.')\n",
    "x_test = np.linspace(1,10,100)\n",
    "x_test = x_test[:,np.newaxis]\n",
    "plt.plot(x_test, lin_reg.predict(poly_reg.fit_transform(x_test)), c='r')\n",
    "plt.title('Truth or Bluff (Polynomial Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.09090909,  1.18181818,  1.27272727,  1.36363636,\n",
       "        1.45454545,  1.54545455,  1.63636364,  1.72727273,  1.81818182,\n",
       "        1.90909091,  2.        ,  2.09090909,  2.18181818,  2.27272727,\n",
       "        2.36363636,  2.45454545,  2.54545455,  2.63636364,  2.72727273,\n",
       "        2.81818182,  2.90909091,  3.        ,  3.09090909,  3.18181818,\n",
       "        3.27272727,  3.36363636,  3.45454545,  3.54545455,  3.63636364,\n",
       "        3.72727273,  3.81818182,  3.90909091,  4.        ,  4.09090909,\n",
       "        4.18181818,  4.27272727,  4.36363636,  4.45454545,  4.54545455,\n",
       "        4.63636364,  4.72727273,  4.81818182,  4.90909091,  5.        ,\n",
       "        5.09090909,  5.18181818,  5.27272727,  5.36363636,  5.45454545,\n",
       "        5.54545455,  5.63636364,  5.72727273,  5.81818182,  5.90909091,\n",
       "        6.        ,  6.09090909,  6.18181818,  6.27272727,  6.36363636,\n",
       "        6.45454545,  6.54545455,  6.63636364,  6.72727273,  6.81818182,\n",
       "        6.90909091,  7.        ,  7.09090909,  7.18181818,  7.27272727,\n",
       "        7.36363636,  7.45454545,  7.54545455,  7.63636364,  7.72727273,\n",
       "        7.81818182,  7.90909091,  8.        ,  8.09090909,  8.18181818,\n",
       "        8.27272727,  8.36363636,  8.45454545,  8.54545455,  8.63636364,\n",
       "        8.72727273,  8.81818182,  8.90909091,  9.        ,  9.09090909,\n",
       "        9.18181818,  9.27272727,  9.36363636,  9.45454545,  9.54545455,\n",
       "        9.63636364,  9.72727273,  9.81818182,  9.90909091, 10.        ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1,10,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
